In [ ]:
import pandas as pd
import plotly.express as px
from IPython.display import display
from typing import List
px.set_mapbox_access_token(open(".mapbox_token").read())
pd.options.display.max_rows = 10

from helpers import  _load_synthetic_clinics, _load_at_risk_zip3, _k_closest_clinics, draw_status_treemap, _load_zip3_census, draw_at_risk_vs_clinic_locations_map

draw_status_treemap()
In [ ]:
def build_distance_matrix(origin_zip3: List[str], clinics: pd.DataFrame, k:int =10) -> pd.DataFrame:
    #  this takes 2 min at 500 clinics / could consider optimizing
    """
        Iterates through each zip3 <> clinic permutation, 
        and selects the min(distance) k clinic locations
    """

    dist_matrix = pd.concat(
        [_k_closest_clinics(zip3, clinics, k=k) for zip3 in origin_zip3]
    ).reset_index(drop=True)
    return dist_matrix

at_risk = _load_at_risk_zip3(adi_floor=20) # I could use alternate perspective on where to tune to
clinics = _load_synthetic_clinics(n=500) # see helpers.py for "Why Synthetic"

at_risk_distances = build_distance_matrix(at_risk['_zip3'], clinics)
at_risk_distances

572 zip3 Location{} at risk with ADI above 20
Out[ ]:
_state _clinic_zip5 _lat _lng _type _origin_zip3 _distance
0 IL 62995 37.419275 -88.879937 synthetic_clinic 301** 293
1 IL 62967 37.584276 -88.735808 synthetic_clinic 301** 296
2 IL 62902 37.674381 -89.112452 synthetic_clinic 301** 315
3 IL 62833 38.340234 -88.167646 synthetic_clinic 301** 320
4 IL 62809 38.278964 -88.337811 synthetic_clinic 301** 321
... ... ... ... ... ... ... ...
5715 CO 80722 40.476059 -103.200495 synthetic_clinic 828** 347
5716 CO 80260 39.866988 -105.001354 synthetic_clinic 828** 348
5717 CO 80744 40.873626 -102.391968 synthetic_clinic 828** 349
5718 CO 80701 40.125042 -103.817561 synthetic_clinic 828** 353
5719 CO 80207 39.761385 -104.916696 synthetic_clinic 828** 356

5720 rows × 7 columns

In [ ]:
def _get_at_risk_stats(at_risk_distances: pd.DataFrame) -> pd.DataFrame:
    at_risk_stats = at_risk_distances.groupby(['_origin_zip3']).agg(
        k=("_distance","count"),
        distance_mean=("_distance","mean"), # mean distance to closest k clinics
        distance_min=("_distance","min"),
        distance_max=("_distance","max")
    ).reset_index()

    def _geocode_zip3(at_risk_stats):
        zip3_geo = _load_zip3_census()[['_zip3','_lat','_lng']]
        return at_risk_stats.rename(columns={"_origin_zip3":"_zip3"}).merge(_load_zip3_census()) #somethings off with _state)
    
    at_risk_stats = _geocode_zip3(at_risk_stats) 
    return at_risk_stats
at_risk_stats = _get_at_risk_stats(at_risk_distances)
at_risk_stats # hmmm something messed up with NY and PA upstream
Out[ ]:
_zip3 k distance_mean distance_min distance_max _state _lat _lng _census_total _adi_mean
0 127** 10 36.4 9 57 NY 41.695786 -74.773225 96155 55.840000
1 127** 10 36.4 9 57 PA 41.485554 -74.892326 1207 47.000000
2 147** 10 37.9 20 63 NY 42.211762 -78.852733 175778 81.120000
3 147** 10 37.9 20 63 PA 42.046328 -79.669830 2540 75.000000
4 150** 10 138.3 81 164 PA 40.457488 -80.065227 448172 65.563380
... ... ... ... ... ... ... ... ... ... ...
638 979** 10 218.4 127 257 OR 43.801827 -117.543657 31751 63.200000
639 990** 10 125.9 67 180 ID 47.635828 -117.094140 6264 41.000000
640 990** 10 125.9 67 180 WA 47.619646 -117.441078 142008 50.387097
641 991** 10 125.5 68 196 ID 47.097949 -117.081329 321 62.000000
642 991** 10 125.5 68 196 WA 47.840974 -118.012785 115238 62.625000

643 rows × 10 columns

In [ ]:
px.box(
    at_risk_stats, x="_state", y="distance_mean", 
    title="Range of Mean Distance to 10 Closest Clinics | by State"
).show(renderer="notebook")
In [ ]:
px.scatter_mapbox(
    at_risk_stats, lat="_lat", lon="_lng", size_max=15, hover_data=['_adi_mean'],
    height=600, zoom=3, size='_census_total', color='distance_mean', 
    title=f"Unprotected Origin Locations | n={len(at_risk_stats)} Zip3 | ADI Mean: {at_risk_stats['_adi_mean'].mean()}"
).show(renderer='notebook')
In [ ]:
px.histogram(
    at_risk_stats, x='distance_mean',
    title=f"On average, a protection seeker would need to travel {at_risk_stats['distance_mean'].mean()} miles "
).show(renderer="notebook")

NB: a good goal here is to find a way to strategically deploy resources to minimize harm¶

In [ ]:
def draw_at_risk_vs_clinic_locations_map() -> None:
    
    """
        Illustrative of Areas with Protections, vs those without
        Why syntethic clinics
    """
    at_risk = _load_at_risk_zip3()
    clinics = _load_synthetic_clinics(n=500)
    locations = pd.concat([clinics,at_risk]).fillna(at_risk['_census_total'].mean())
    px.scatter_mapbox(
        locations, lat="_lat", lon="_lng", size_max=15, mapbox_style="open-street-map",
        height=700, zoom=3, color='_state', hover_data=['_state','_zip3'],
        size='_census_total',
        title=f"At Risk Areas (3-Digit Zipcode) vs (Synthetic) Clinic Locations | Scaled by Population"
    ).show(renderer='notebook')
    return locations

draw_at_risk_vs_clinic_locations_map()

498 zip3 Location{} at risk with ADI above 50
Out[ ]:
_state _zip5 _clinic_geo _lat _lng _type index _zip3 _census_total _adi_mean
0 ME 04276 (44.5599203, -70.6252639) 44.559920 -70.625264 synthetic_clinic 275284.803213 275284.803213 275284.803213 275284.803213
1 WA 99109 (48.2924161, -117.6986398) 48.292416 -117.698640 synthetic_clinic 275284.803213 275284.803213 275284.803213 275284.803213
2 MN 55129 (44.8834738, -92.8927678) 44.883474 -92.892768 synthetic_clinic 275284.803213 275284.803213 275284.803213 275284.803213
3 CA 93618 (36.5221175, -119.3866282) 36.522118 -119.386628 synthetic_clinic 275284.803213 275284.803213 275284.803213 275284.803213
4 CO 81653 (40.8957476, -107.2422296) 40.895748 -107.242230 synthetic_clinic 275284.803213 275284.803213 275284.803213 275284.803213
... ... ... ... ... ... ... ... ... ... ...
493 WV 275284.803213 275284.803213 38.966814 -78.995744 at_risk 955.000000 268** 33282.000000 66.500000
494 WY 275284.803213 275284.803213 42.338740 -104.575799 at_risk 961.000000 822** 24524.000000 51.421053
495 WY 275284.803213 275284.803213 41.804052 -106.980921 at_risk 962.000000 823** 15430.000000 67.400000
496 WY 275284.803213 275284.803213 44.438020 -108.408179 at_risk 963.000000 824** 52930.000000 51.636364
497 WY 275284.803213 275284.803213 43.103802 -108.847958 at_risk 964.000000 825** 38910.000000 60.888889

998 rows × 10 columns

_archive¶

below is archive and wip

Simulation: Distance Traveled¶

Goal is to learn what the range of experiences will be as the experience of someone seeking care from an origin point in PA for example...

In [ ]:
def draw_closest_clinics_by_state(states: list):
    at_risk = _load_at_risk_zip3()
    _at_risk = at_risk[at_risk['_state'].isin(states)]
    locations = pd.concat([_at_risk, _load_synthetic_clinics()])

    px.scatter_mapbox(
        locations, lat="_lat", lon="_lng", size_max=15, 
        height=600, zoom=3, color='_type', 
        title=f"At Risk Areas vs (Simulated) 10 Closest Clinic Locations"
    ).show(renderer='notebook')

draw_closest_clinics_by_state(["PA",'LA'])

498 zip3 Location{} at risk with ADI above 50